Vol.3, No.2, 2021, pp.79-88, doi:10.32604/jcs.2021.017082
OPEN ACCESS
ARTICLE
Pedestrian Crossing Detection Based on HOG and SVM
  • Yunzuo Zhang*, Kaina Guo, Wei Guo, Jiayu Zhang, Yi Li
School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China
* Corresponding Author: Yunzuo Zhang. Email:
Received 25 January 2021; Accepted 24 May 2021; Issue published 02 August 2021
Abstract
In recent years, pedestrian detection is a hot research topic in the field of computer vision and artificial intelligence, it is widely used in the field of security and pedestrian analysis. However, due to a large amount of calculation in the traditional pedestrian detection technology, the speed of many systems for pedestrian recognition is very limited. But in some restricted areas, such as construction hazardous areas, real-time detection of pedestrians and cross-border behaviors is required. To more conveniently and efficiently detect whether there are pedestrians in the restricted area and cross-border behavior, this paper proposes a pedestrian cross-border detection method based on HOG (Histogram of Oriented Gradient) and SVM (Support Vector Machine). This method extracts the moving target through the GMM (Gaussian Mixture Model) background modeling and then extracts the characteristics of the moving target through gradient HOG. Finally, it uses SVM training to distinguish pedestrians from nonpedestrians, completes the detection of pedestrians, and labels the targets. The test results show that only the HOG feature extraction of the candidate area can greatly reduce the amount of calculation and reduce the time of feature extraction, eliminate background interference, thereby improving the efficiency of detection, and can be applied to occasions with real-time requirements.
Keywords
Pedestrian detection; HOG; SVM; GMM
Cite This Article
Zhang, Y., Guo, K., Guo, W., Zhang, J., Li, Y. (2021). Pedestrian Crossing Detection Based on HOG and SVM. Journal of Cyber Security, 3(2), 79–88.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.